The preceding examples focus on parameter estimation in the image domain. In contrast, [1]} have used ML algorithms for extracting the parameters of the 21-cm power spectrum; however, they have not considered foregrounds in their analysis. This has gap has been addressed in [2]}, who employed ANN to extract parameters from a 21-cm Global signal under more realistic conditions. They used bright galactic foregrounds and instrumental corruption to estimate astrophysical parameters at modest computational costs. Their parameter estimation method gave \(\gtrsim \) 92% accuracy, even for corrupted data sets. In [3]}, they have developed ANN models to directly extract astrophysical characteristics from 21-cm Global signal observations, using physically justified 21-cm signal and foreground models. They have further developed their network in [4]} to extract the 21-cm PS and the corresponding EoR parameters from synthetic observations. There are currently efforts to include more realistic effects like ionosphere and primary beam in the training process to produce networks that can predict parameters from corrupted data sets (Tripathi et al. in prep). Once perfected, these would be incorporated into the pipeline described above for recovering parameters from the synthetic observations using realistic conditions.
